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Conditional Statistical Inference with Multistage Testing Designs

Published online by Cambridge University Press:  01 January 2025

Robert J. Zwitser  and
Gunter Maris
Robert J. Zwitser*
Affiliation:
Cito Institute for Educational Measurement
Gunter Maris
Affiliation:
Cito Institute for Educational Measurement and University of Amsterdam
*
Requests for reprints should be sent to Robert J. Zwitser, Psychometric Research Center, Cito Institute for Educational Measurement, P.O. Box 1034, 6801 MG, Arnhem, The Netherlands. E-mail: robert.zwitser@cito.nl
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Abstract

In this paper it is demonstrated how statistical inference from multistage test designs can be made based on the conditional likelihood. Special attention is given to parameter estimation, as well as the evaluation of model fit. Two reasons are provided why the fit of simple measurement models is expected to be better in adaptive designs, compared to linear designs: more parameters are available for the same number of observations; and undesirable response behavior, like slipping and guessing, might be avoided owing to a better match between item difficulty and examinee proficiency. The results are illustrated with simulated data, as well as with real data.

Keywords

multistage testingadaptive testingitem response theoryparameter estimationconditional maximum likelihood

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 1 , March 2015 , pp. 65 - 84
Copyright
Copyright © 2013 The Psychometric Society

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